Figure REF  shows a schematic diagram of the structure of the Arrhenius.jl package. The Arrhenius.jl reads in the chemical mechanism files in YAML format maintained by the Cantera community, and the chemical mechanism files contain the kinetic models, thermodynamic, and transport databases. The core functionality of Arrhenius.jl is to compute the reaction source terms and mixture properties, such as heat capacities, enthalpies, entropies, Gibbs free energies, etc. In addition, Arrhenius.jl offers flexible interfaces for users to define neural network models as submodels and augment them with existing physical models. For example, one can use a neural network submodel to represent the unknown reaction pathways and exploit various scientific machine learning methods to train the neural network models, such as neural ordinary differential equations [1]}, [2]} and physics-informed neural network models [3]}, [4]}. One can then implement the governing equations for different applications with these core functionalities and solve the governing equations using classical numerical methods or neural-network-based solvers, such as physics-informed neural networks [3]}, [4]}. Arrhenius.jl provides solvers for canonical combustion problems, such as simulating the auto-ignition in constant volume/pressure reactors and oxidation in jet stirred reactors.
